After co-founding an AI company in the climate finance space, Pioneer, where we helped clients win $175+ million in grants and tax credits in <18 months, one thing became clear: AI alone isn’t enough.

 

In many specialized industries, human expertise still plays a critical role. But the upside for startups that combine AI with specialized human know-how is massive, especially in overlooked “niche” markets that VCs have historically written off as too small.

Although AI has advanced quickly, it can’t (yet) replace human insight in many high-stakes or highly specialized workflows. Clients care about outcomes and trust; if the result is mission-critical or financially significant, they want accountability from people, not just machines. Net promoter score (NPS), retention, and account expansion hinges on having experienced experts who can guide, interpret, and validate AI-driven work. That personal relationship and trust factor makes all the difference.

Traditionally, software solutions in niche verticals suffered from low average contract values (ACVs). You might sell a $10,000-per-year tool to automate part of a workflow. But in the AI era, the math changes.

 

Instead of selling a tool that supports existing teams, AI startups can outright replace or augment entire roles. That means you can price your solution in line with what a customer would otherwise spend on headcount—tens or hundreds of thousands of dollars per year.​

 

The difference between a $10k software license and a $100k–$1M “AI + expert” service is transformative. A niche market with only 10,000 potential customers can suddenly support a TAM leap from $100M to low billions, based solely on bigger ACVs.

Beyond higher ACVs, smarter AI + human solutions can expand the total market itself. Think about Uber’s impact on the taxi industry: by improving convenience, speed, and cost, Uber unlocked a wave of new users who rarely took cabs before. Similarly, an AI-powered service that reduces complexity or cost can attract brand-new customers who previously didn’t see enough value in the legacy approach.
 

This is an amazing time to build an AI startup. Many industries that were previously considered “small TAM” for venture investment are now wide open. These customers aren’t living under a rock—they see what’s happening in AI and many are eager early adopters. That creates an opportunity to move fast before others catch on. Here's a simple approach for how to sequence the steps.

​

1. Focus on niche verticals that were previously overlooked by software founders and investors due to small TAM.

2. Take on headcount scope, replace work, not just assist it, and charge accordingly. But be sure to start narrow, then expand methodically over time.

3. Increase the distribution of work the AI does over time, maintaining human oversight and client relationships to ensure quality and build trust.

4. Look beyond today’s market size. Think about how AI can expand the market the way Uber did for ride-sharing.

​

We’re at a moment when vertical AI startups can redefine what “market size” really means. By combining highly efficient AI with the human quality and trust factors—and by charging in line with headcount budgets—you can multiply ACVs, expand market scope, and deliver solutions that feel revolutionary to historically underserved customers. Don’t overlook niche industries: the TAM is bigger than you think, and the window to build a category-defining AI company in these spaces has never been wider.

​

This thesis is still being tested in real time. If you’re a founder, investor, or engineer exploring AI in overlooked industries, reach out at kyle.treige@gmail.com. I’d love to learn from what you’re seeing.